class_docstr: Polynomial interpolation between `initial_p` and `final_p`.
  
  Over `schedule_timesteps`. After this many time steps, always returns
  `final_p`.
docstr: Returns the result of:
  final_p + (initial_p - final_p) * (1 - `t`/t_max) ** power
docstr_tok: ["Returns","the","result","of",":","final_p","+","(","initial_p","-","final_p",")","*","(","1","-","`","t","`","\/t_max",")","*","*","power"]
code: def _value(self, t: TensorType) -> TensorType:
      """Returns the result of:
      final_p + (initial_p - final_p) * (1 - `t`/t_max) ** power
      """
      if (
          self.framework == "torch"
          and torch
          and isinstance(t, torch.Tensor)
      ):
          t = t.float()
      t = min(t, self.schedule_timesteps)
      return (
          self.final_p
          + (self.initial_p - self.final_p)
          * (1.0 - (t / self.schedule_timesteps)) ** self.power
      )
  
file_path: ray/rllib/utils/schedules/polynomial_schedule.py
imports: from typing import Optional
  from ray.rllib.utils.annotations import override, PublicAPI
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.utils.schedules.schedule import Schedule
  from ray.rllib.utils.typing import TensorType
label_desc: Use image node_id 2 for calling the PolynomialSchedule obj's underlying member method code with example usage: obj._value(t) and returns: unknown